#import important libraries
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
pio.templates.default = "plotly_white"

#read the downloaded csv file
data = pd.read_csv('Downloads/rides.csv')
print(data.head())

   Drivers Active Per Hour  Riders Active Per Hour  Rides Completed
0                       72                     295            202.0
1                       50                      78             43.0
2                       40                     250            181.0
3                       78                     140            124.0
4                       74                     195            108.0

#see if the dataset has null values
print(data.isnull().sum())

Drivers Active Per Hour     0
Riders Active Per Hour      0
Rides Completed            54
dtype: int64

#drop all the null values and recheck if all null values are removed 
data = data.dropna()
print(data.isnull().sum())

Drivers Active Per Hour    0
Riders Active Per Hour     0
Rides Completed            0
dtype: int64

#Analyze the relationship between the number of drivers active per hour and
#the number of riders active per hour

demand = data["Riders Active Per Hour"]
supply = data["Drivers Active Per Hour"]

figure = px.scatter(data, x = "Drivers Active Per Hour",
                          y = "Riders Active Per Hour",
                  trendline = "ols",
                      title = "Demand and Supply Analysis")

figure.update_layout(
    xaxis_title = "Numbers of Drivers Active Per Hour (Supply)",
    yaxis_title = "Numbers of Riders Active Per Hour (Demand)")
figure.show()

#From the above fig we can see a constant relationship between the number of drivers per hour 
#and the number of riders active per hour

#Now let's calculate the elasticity of demand for rides concerning the number of active drivers
#per hour

#Calculate elasticity
avg_demand = data["Riders Active Per Hour"].mean()
avg_supply = data["Drivers Active Per Hour"].mean()
pct_change_demand = (max(data["Riders Active Per Hour"]) - min(data["Riders Active Per Hour"])) / avg_demand * 100
pct_change_supply = (max(data["Drivers Active Per Hour"]) - min(data["Drivers Active Per Hour"])) / avg_supply * 100
elasticity = pct_change_demand / pct_change_supply

print("Elasticity of demand with respect to the number of active drivers per hour : {:.2f}".format(elasticity))

Elasticity of demand with respect to the number of active drivers per hour : 0.82

#Now let's add a new column in the dataset by calculating the supply ratio
#Calculate the supply ratio for each level of driver activity

data["Supply Ratio"] = data["Rides Completed"] / data["Drivers Active Per Hour"]
print(data.head())

   Drivers Active Per Hour  Riders Active Per Hour  Rides Completed  \
0                       72                     295            202.0   
1                       50                      78             43.0   
2                       40                     250            181.0   
3                       78                     140            124.0   
4                       74                     195            108.0   

   Supply Ratio  
0      2.805556  
1      0.860000  
2      4.525000  
3      1.589744  
4      1.459459  

#Now let's visualize the supply ratio

fig = go.Figure()
fig.add_trace(go.Scatter(x=data["Drivers Active Per Hour"],
                         y=data["Supply Ratio"], mode ="markers"))
fig.update_layout(
        title = "Supply Ratio vs Driver Activity",
  xaxis_title = "Driver Activity(Drivers Active Per Hour)",
  yaxis_title = "Supply Ratio (Rides Completed per Driver Active per Hour)"
)
fig.show()

#The above graph shows the ratio of the number of drivers active per hour and the number of rides 
#completed in an hour. So this is how we can analyze demand and supply using the Python programming 
#language.

#Summary
#Demand and Supply analysis means analyzing the relationship between the quantity demanded and 
#the quantity supplied. It helps businesses understand the factors influencing consumer demand 
#to maximize profits.